Abstract
In this paper, an Enhanced Imperialist Competitive (EIC) Algorithm is proposed for solving reactive power problem. Imperialist Competitive Algorithm (ICA) which was recently introduced has shown its decent performance in optimization problems. This innovative optimization algorithm is inspired by socio-political progression of imperialistic competition in the real world. In the proposed EIC algorithm, the chaotic maps are used to adapt the angle of colonies movement towards imperialist’s position to augment the evading capability from a local optima trap. The ICA is candidly stuck into a local optimum when solving numerical optimization problems. To overcome this insufficiency, we use four different chaotic maps combined into ICA to augment the search ability. Proposed Enhanced Imperialist Competitive (EIC) algorithm has been tested on standard IEEE 30 bus test system and simulation results show clearly the decent performance of the proposed algorithm in reducing the real power loss.
Highlights
Optimal reactive power dispatch (ORPD) problem is a multi-objective optimization problem that minimizes the real power loss and bus voltage deviation
In [16], the optimal reactive power flow problem is solved by integrating a genetic algorithm with a nonlinear interior point method
This paper proposes a new Enhanced Imperialist Competitive Algorithm (EIC) to solve the optimal reactive power problem
Summary
Optimal reactive power dispatch (ORPD) problem is a multi-objective optimization problem that minimizes the real power loss and bus voltage deviation. Various mathematical techniques like the gradient method [1,2], Newton method [3] and linear programming [4,5,6,7] have been adopted to solve the optimal reactive power dispatch problem Both the gradient and Newton methods have the complexity in managing inequality constraints. Global optimization has received extensive research awareness, and a great number of methods have been applied to solve this problem Evolutionary algorithms such as genetic algorithm have been already proposed to solve the reactive power flow problem [9, 10]. In [16], the optimal reactive power flow problem is solved by integrating a genetic algorithm with a nonlinear interior point method. Where ng, nt and nc are the number of generators, number of tap transformers and the number of shunt compensators respectively
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